CN108078540B - A set of flora interaction network markers capable of screening disease-related flora and application thereof - Google Patents
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Abstract
The invention discloses a set of flora interaction network markers capable of screening disease-related flora and application thereof, wherein the markers are composed of 15 special triangular interaction network motifs. The method takes the composition of bacterial species of a flora and the relative abundance of each bacterial species as input, and judges whether the flora to be detected is abnormal or not according to the number of target markers obtained by statistics and the numerical Ratio (RDHT) of the residual target markers of the flora to be detected and the normal flora based on a flora interaction network and 15 special triangular interaction network motifs. The invention is verified by data of a plurality of groups of microbial floras of 6 parts of human bodies including intestinal tracts, vaginas, lungs, oral cavities, semen and skins, and finds that the number of special triangular interaction network motifs in a microbial floras interaction network is obviously different between an experimental group/a disease group and a healthy control group.
Description
Technical Field
The invention relates to the fields of bioinformatics technology and medical health, in particular to a flora interaction network marker capable of screening disease-related flora and a device for screening disease-related flora on the basis of the marker.
Background
The human symbiotic bacteria are normal flora of human body, and a large amount of microorganisms exist in skin, oral cavity, intestinal tract, respiratory tract, urogenital tract and other parts. The human microbial flora has close relationship with the health and diseases of hosts, and meanwhile, the health condition of human body also influences the composition and stability of the microbial flora of organisms. With the development of Human microbial metagenome Project (HMP), more and more studies have shown that microbial flora is closely related to the occurrence and development of various diseases, such as colon cancer, pulmonary fibrosis, periodontitis, mastitis, bacterial vaginosis and dermatitis, and these diseases are also called "microbial flora related diseases". The diseases are mostly faced with the problems of lack of early diagnosis indexes, more convenient and reliable personalized diagnosis methods and the like, so that the search for a personalized diagnosis method which can be simultaneously applied to various diseases is very important.
The human microbial flora can further search the root cause of the occurrence and development of related diseases of microorganisms and can be used for personalized diagnosis and prevention and treatment of the diseases. As a microscopic ecosystem, the existing research is mostly carried out by utilizing ecological theory and methods from the ecological point of view, wherein the diversity of species is the most common research means. However, a large number of studies have revealed that there is no significant difference in the diversity index of microbial species, such as species abundance, Shannon index, etc., between healthy individuals and diseased individuals, and thus it cannot be widely used for the assessment of diagnostic health status of various diseases. The diversity-based ecological method mostly focuses on the number of species, but ignores the interaction among species, and the interaction among species has more sensitive strain capacity to the change of the external environment or the host environment.
Although network analysis has been widely applied to computational biology and bioinformatics, such as gene networks, protein networks, metabolic networks, etc., the application in the research of microbial flora is still relatively limited. The network analysis searches the interaction relation among the species based on the individual number or abundance of the species, not only includes the species abundance in the traditional ecological analysis, but also adds the interaction relation in the flora, and can make up the defects of the traditional ecological analysis such as diversity analysis in microbial ecology and the like. Network analysis has been successfully applied to the human microbial flora. In the traditional network attributes such as network density and the like, most of the information of vertexes in the network is ignored, each vertex in the microbial flora network represents a species, and the interaction relationship between the species may be different even if the structures of the microbial flora interaction networks of diseases and healthy organisms are the same. In the analysis of the basic network, the network attribute including the species information can improve the accuracy and the sensitivity of the diagnosis index and the detection method.
The invention aims to provide and verify a method for analyzing and evaluating the health condition of an organism and diagnosing diseases based on a human flora interaction network aiming at the evaluation of the health of the organism and the personalized diagnosis of diseases related to microbial flora, and finds out 15 special network motifs as a new attribute of the microbial flora interaction network through the analysis of the existing literature and the results of a large number of experiments. By monitoring the change condition of the attribute in the organism flora network, a more effective microorganism method can be provided for evaluating the health condition of the organism and the occurrence risk of diseases, and reliable technical support is provided for the personalized diagnosis of the relevant diseases of the microorganism flora.
Disclosure of Invention
The invention aims to provide and verify a method for analyzing and evaluating the health of organisms and diagnosing diseases based on a human flora interaction network aiming at the evaluation of the health of the organisms and the personalized diagnosis of microbial flora related diseases including obesity and other diseases, and provides an effective method and a reliable index for evaluating the health condition of the human body and diagnosing the microbial flora related diseases by using human microbes.
In order to realize the purpose, the invention adopts the technical scheme that:
6 groups of microorganism data are obtained by searching and screening, and respectively represent microorganisms of 6 human body parts such as intestinal tracts, oral cavities, skins, lungs, vaginas, male genital glands and the like, and the data sources and information are as follows: microbial data of intestinal tracts of HIV positive and negative people published in 2013 by McHardy et al; oral salivary microbial data published in 2010 by Lazarevic et al for smokers and non-smokers; skin microbial data published by Kong et al in 2012 for Atopic Dermatitis (AD) patients and healthy persons; microbial data of respiratory tract in patients with pulmonary fibrosis in acute exacerbation and after receiving treatment, published in 2012 by Fodor et al; vaginal microbial data of Bacterial Vaginosis (BV) patients and healthy women published by Srinivasan et al in 2012; and sperm microbial data of male infertility patients and healthy males published in 2013 by Hou et al.
Based on the abundance of microbial species (namely sequencing content corresponding to each species), the correlation coefficients between the disease group and each species of the health group or each operation classification unit (OTU) in the 6 groups of data are respectively calculated, the interaction relation of p less than or equal to 0.05 is selected, and a flora interaction network diagram is constructed by using standard network analysis software, such as Cytoscape software or iGraph software.
Based on the flora interaction network constructed by the steps, 15 special trios shown in the table 1 in each group are searched. In the table, the legend represents a motif structure model, each network node represents a microorganism species or operation classification unit (OTU), the solid nodes represent OTUs with special functions, and the hollow nodes represent common OTUs. The special function node represents the most abundant species in the flora, or the dominant species in the flora, or the species with the most interaction relationship in the flora interaction network. The edges are the interaction relation between the OTUs represented by the two nodes, and the special function node branches represent the edges connecting the special function nodes and the triangle trios formed by the common nodes. The "+" sign in the table indicates a positive interaction relation, "-" indicates a negative interaction relation, "+" and "-" in table 1.1 indicate the interaction situation of three sides in the triangle trios, and "+" and "-" in table 1.2 indicate the interaction situation of the branches of the special function node. The 15 trios are divided into two main classes, one is three-point trios without special function node branches, and the other is four-point trios with special function node branches. In the present invention, all the special functional points are OTU (most abundance OTU, MAO) with the highest abundance in the flora. The former is divided into four types I, II, III and IV according to the positive-negative relationship of the interaction among the points, wherein the types I and II are divided into two subtypes A and B through the positive-negative relationship among 2 non-MAO nodes; the latter is divided into three subtypes, SLM, DLM and TLM, according to the direct action relationship or number of the edges with MAO, and is divided into several subtypes according to the positive and negative conditions of the direct action relationship with MAO.
TABLE 1.16 structural information of network motifs without special vertex branches
TABLE 1.29 structural information of network motifs with specific vertex branching
By comparing the amount of trios of each type in the disease group and healthy group flora interaction networks in the 6 groups of data, it was found that these network structures were significantly different between disease patients and healthy persons. The health condition of an organism can be detected by detecting the amount of trios in the microflora interaction network, and the change of the amount of trios in the microflora interaction network in a specific organism part can also be used for diagnosing microorganism-related diseases. The method provides a method and an index for evaluating the health condition of a human body by using human microorganisms, and makes a certain contribution to early diagnosis and prevention of diseases.
The invention has the advantages of providing a set of flora interaction network markers capable of screening disease-related flora and a device for preparing the disease-related flora screening device by using the set of interaction network markers. The invention can effectively and quickly screen the healthy and disease flora, provides effective reference indexes for evaluating the health of organisms and the occurrence risk of diseases, and provides reliable technical support for the personalized diagnosis of related diseases of the microbial flora.
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FIG. 1 shows a flow chart of the present invention.
Detailed Description
The present invention will be further described with reference to the following examples, but is not limited thereto.
Example 1: comparison of the number of specific trios in the intestinal flora network of HIV-positive and HIV-negative individuals
The calculation flow of the invention is shown as the attached figure 1
The data source is as follows:
McHardy et al published microbial data on the intestinal tract of HIV positive and negative patients in 2013, samples were taken from 20 HIV patients who did not receive antiretroviral therapy and 20 healthy human intestinal mucosa. Amplifying a 16S rDNA fragment from a sample DNA by using a universal primer, sequencing by using an Illumina HiSeq 2000 sequencing platform, and obtaining a microbial operation classification unit (OTU) with 97% similarity through subsequent bioinformatics analysis, wherein each OTU represents a species, and the sequencing content of the OTU in each sample represents the species abundance of the OTU in the sample.
Constructing an intestinal flora interaction network:
respectively calculating Spearman correlation coefficients R between OTUs in intestinal flora of an HIV positive group and an HIV negative control group on the basis of OTU abundance, and selecting an interaction relation that p is less than or equal to 0.05, wherein the correlation coefficient R is less than 0 and represents that the OTUs are in a negative interaction relation, and the correlation coefficient R is more than 0 and represents that the OTUs are in a positive interaction relation.
Outputting the number of triangle motifs in the intestinal flora network of HIV positive and negative and RDHT values:
the number of 15 triangle motifs in the intestinal flora interaction network was exported, and the results are shown in Table 2, except type II, the number of triangle motifs of all other types in the flora network of HIV-positive patients was smaller than that of HIV-negative control group. If the generation of a disease does not have any influence on the organism flora, the number of triangle motifs in the flora network of the disease patient is similar to that of healthy people, and the ratio of the number of tiros in the disease group to the number of triangle motifs in the healthy group (RDHT) is 1. The RDHT values of each triangle motif type are calculated respectively, and the results are shown in Table 2, the RDHT values are all less than 1, wherein the RDHT values of I, II, SLM, DLM and TLM types are all far less than 1, which indicates that the triangle motif of the intestinal flora interaction network of HIV positive patients and HIV negative patients have significant difference.
TABLE 2 number of triangle motifs in the HIV-positive and HIV-negative microbiota interaction network
Example 2: comparing the number of specific trios in an oral flora interaction network between smokers and non-smokers
The calculation flow of the invention is shown as the attached figure 1
The data source is as follows:
lazarevic et al published oral salivary microbial data for smokers and non-smokers in 2010, and oral saliva samples were taken from two non-smoking individuals and 3 smoking individuals at three time points over 29 days, respectively. Amplifying a 16S rDNA fragment from a sample DNA by using a universal primer, sequencing the sequence in a Genome sequence FLX system, and obtaining a microorganism operation classification unit (OTU) with 97% similarity through subsequent bioinformatics analysis, wherein each OTU represents a species, and the sequencing content of the OTU in each sample represents the species abundance of the OTU in the sample.
Constructing an oral flora interaction network:
respectively calculating Spearman correlation coefficients R between the OTUs in the oral salivary flora of the smoking group and the non-smoking group on the basis of the abundance of the OTUs, and selecting an interaction relation that p is less than or equal to 0.05, wherein the correlation coefficient R is less than 0 and represents that the OTUs are in a negative interaction relation, and the correlation coefficient R is more than 0 and represents that the OTUs are in a positive interaction relation.
Outputting the number of triangle motifs in the oral flora network and the RDHT values of smokers and non-smokers:
the results are shown in Table 3, and the numbers of triangle motifs of type I-A, type IV, DLM-II, DLM-III, TLM-III and TLM-IV in the oral flora network of smokers are significantly higher than those of non-smokers, and the numbers of triangle motifs of type I-B and SLM are lower than those of non-smokers, except for type II and type III. The RDHT values for each type of triangle motif were calculated separately and as shown in table 3, all the RDHT values deviate far from 1, indicating that there was a significant difference in the number of triangle motifs in the oral flora interaction network compared to non-smoking smokers.
TABLE 3 number of triangle motifs in the HIV-positive and HIV-negative microbiota interaction network
Example 3: comparison of the number of specific trios in the skin flora interaction network between AD patients and healthy persons
The calculation flow of the invention is shown as the attached figure 1
The data source is as follows:
kong et al published the skin microbial data of AD patients and healthy people in 2012, and samples were taken from 12 AD patients aged between 2-15 years and 11 healthy people's skin samples. Amplifying a 16S rDNA fragment from a sample DNA by using a universal primer, sequencing, and analyzing by bioinformatics to obtain microbial operation classification units (OTUs) with 97% similarity, wherein each OTU represents a species, and sequencing content of the OTU in each sample represents species abundance of the OTU in the sample.
Constructing a skin flora interaction network:
respectively calculating the Spearman correlation coefficient R between OTUs in the skin bacterium groups of the AD group and the healthy group on the basis of the abundance of the OTUs, and selecting an interaction relation with p less than or equal to 0.05, wherein the correlation coefficient R less than 0 represents that the OTUs are in a negative interaction relation, and R more than 0 represents that the OTUs are in a positive interaction relation.
Outputting the number of triangle motifs and RDHT values in the skin flora interaction network of AD patients and healthy people:
the number of 15 triangle motifs in the skin flora interaction network was exported, and as a result, only two types, I-B type and TLM-I type, of specific triangle motifs were detected in the skin flora network of AD patients, while no triangle motif containing the peak of MAO function was detected in healthy skin flora, as shown in table 4. From the RDHT values of each type of triangle motif in Table 4, it can be more intuitively determined that there is a significant difference in the number of specific triangle motifs between AD patients and healthy human skin flora networks.
TABLE 4 number of triangle motifs in the HIV-positive and HIV-negative hygiene flora interaction network
Example 4: comparing the amount of specific trios in the interactive network of flora in respiratory tract after treatment with the acute exacerbation period of patients with pulmonary fibrosis
The calculation flow of the invention is shown as the attached figure 1
The data source is as follows:
sputum microorganism data in the respiratory tract of 23 patients with pulmonary fibrosis in the acute exacerbation period and after antibiotic treatment are published in 2012 by Fodor et al. Amplifying a 16S rDNA fragment from a sample DNA by using a universal primer, sequencing by using a 454-FLX chemistry sequencing platform, and performing subsequent bioinformatics analysis to obtain a microbial operation classification unit (OTU) with 97% similarity, wherein each OTU represents a species, and the sequencing content of the OTU in each sample represents the species abundance of the OTU in the sample.
Constructing a respiratory tract flora interaction network:
respectively calculating Spearman correlation coefficients R among the OTUs in the group of groups of sputum flora on the basis of the abundance of the OTUs, and selecting an interaction relation that p is less than or equal to 0.05, wherein the correlation coefficient R is less than 0 and represents that the OTUs are in a negative interaction relation, and R is more than 0 and represents that the OTUs are in a positive interaction relation.
Outputting the number of triangle motifs in the flora interaction network in the respiratory tract and RDHT value after treatment in the acute exacerbation period of the patients with pulmonary fibrosis:
the number of 15 triangle motifs in the respiratory tract sputum flora interaction network was exported, and as a result, as shown in Table 5, type I-A, type IV, SLM, DLM-II and-III, and TLM-III and-IV triangle motifs were detected in the respiratory tract of patients with acute exacerbation pulmonary fibrosis, whereas only SLM-A triangle motif was found in the sputum of patients after antibiotic treatment. The obvious difference between the number of specific triangle motifs in the flora interaction network in the acute exacerbation period of patients with pulmonary fibrosis and after antibiotic treatment is shown more visually by the RDHT value in Table 5 (RDHT > 1). The disease condition of the patient is improved after the antibiotic treatment, and the change of the triangle motifs sensitively reflects the recovery and development of the disease condition of the patient.
TABLE 5 number of triangle motifs in the HIV-positive and HIV-negative microbiota interaction network
Example 5: comparison of specific trios number in vaginal flora interaction network between BV patients and healthy women
The calculation flow of the invention is shown as the attached figure 1
The data source is as follows: vaginal microbial data were published in 2012 by Srinivasan et al for BV patients and healthy women, and samples were taken from vaginal samples of 98 BV patients and 121 healthy people. A16S rDNA fragment is amplified from sample DNA by using a universal primer, and after 454FLX pyrosequencing, an operational microbial operational classification unit (OTU) with 97% similarity is obtained through bioinformatics analysis, wherein each OTU represents a species, and the sequencing content of the OTU in each sample represents the species abundance of the OTU in the sample.
Constructing a vaginal flora interaction network:
respectively calculating a Spearman correlation coefficient R between OTUs in a BV patient and a healthy human vaginal flora on the basis of OTU abundance, and selecting an interaction relation that p is less than or equal to 0.05, wherein the correlation coefficient R is less than 0 and represents that the OTUs are in a negative interaction relation, and R is more than 0 and represents that the OTUs are in a positive interaction relation.
Outputting the number of triangle motifs in the vaginal flora interaction network and RDHT values for BV patients and healthy women:
the number of 15 triangle motifs in the vaginal flora interaction network was exported and the results are shown in Table 6, except for type II-A and type III, all other triangle motifs were detected in the vaginal flora interaction network of BV patients, where only type SLM-II was slightly smaller than that of healthy group and all the others were larger than that of healthy group. By calculating the RDHT values of various triangle motifs, the RDHT values of the BV groups of type I, type II-B, type IV, SLM-I, DLM-I and TLM are all far greater than 1, which indicates that the triangle motifs have significant difference in the BV patient and healthy female vaginal flora interaction network.
TABLE 6 number of triangle motifs in the HIV-positive and HIV-negative microbiota interaction network
Example 6: comparison of the amount of specific trios in the interaction network of the seminal flora between infertile and healthy males
The calculation flow of the invention is shown as the attached figure 1
The data source is as follows:
semen microbial data of male infertility patients and healthy men published in 2013 by Hou et al were collected from semen samples of 33 patients (AS) with oligoasthenospermia infertility, 25 patients (SS) with severe oligoasthenospermia infertility and 15 healthy people. A16S rDNA fragment is amplified from a sample DNA by using a universal primer, and after the 16S rDNA fragment is subjected to Roche 454 GS-FLX pyrophosphoric acid sequencing, microbial operational classification units (OTUs) with 97% similarity are obtained through bioinformatics analysis, wherein each OTU represents a species, and the sequencing content of the OTU in each sample represents the species abundance of the OTU in the sample.
Constructing a semen flora interaction network:
respectively calculating a Spearman correlation coefficient R between OTUs in an infertility patient and a healthy male semen flora on the basis of OTU abundance, and selecting an interaction relation that p is less than or equal to 0.05, wherein the correlation coefficient R is less than 0 and represents that the OTUs are in a negative interaction relation, and the correlation coefficient R is more than 0 and represents that the OTUs are in a positive interaction relation.
Outputting the number of triangle motifs and RDHT value in the semen flora interaction network of the infertility patients and healthy men:
the number of 15 triangle motifs in the exported seminal flora interaction network was found in Table 7, while the SLM-I type triangle motif was found in all three groups of the interaction network, the type I, type IV, DLM type and TLM type triangle motifs were found in the AS group and SS group, and the number of type I, SLM-I, DLM type and TLM-I, -II and-III triangle motifs was higher in the SS group than in the AS group. As shown in Table 7, the RDHT values of the AS group and the healthy group are far higher than 1 except the SLM-I type, and the RDHT values of the SS group and the healthy group are also larger than 1, which indicates that the number of triangle motifs in the semen flora interaction network of the infertility patients is obviously different from that of healthy men. In addition, the RDHT values of the SS group and AS group which are not equal to 1 are all much higher than 1, and it can be seen that the difference between the number of triangle motifs in the patient's flora interaction network and healthy people is more significant with the increase of disease.
TABLE 7 number of triangle motifs in the HIV-positive and HIV-negative microbiota interaction network
Claims (2)
1. The application of a set of flora interaction network markers in the preparation of a device for screening disease-related flora is characterized in that:
the marker consists of 15 special triangular interaction network motifs, wherein the motif information is shown in table 1, the table 1 comprises a table 1.1 and a table 1.2, and the legend in the table represents the topological structure of the motif; based on the topology of the motifs, 15 motifs are divided into 7 large: the I-IV type consists of three vertexes and does not comprise special vertex branches; the SLM, DLM and TLM types are composed of four vertexes, including special vertex branches; each vertex of the motif is a bacterium represented by the operational taxonomic unit, the solid vertices represent bacteria with specific functions, and the hollow vertices represent common bacteria; the special functional bacteria include: the most abundant bacteria in the community, or the dominant species in the community, or the most abundant species in the interaction network; the side is the interaction relation between two vertexes of bacteria, and the branch of the vertex with special function represents the side connecting the vertex with a triangular motif consisting of common vertexes; in the table, "+" denotes a positive interaction relationship, "-" denotes a negative interaction relationship, "+" and "-" in table 1.1 denote interactions of three sides in a triangle motif, and "+" and "-" in table 1.2 denote interactions between two vertices to which special-function vertex branches are connected;
TABLE 1.16 structural information of network motifs without special vertex branches
TABLE 1.29 structural information of network motifs with specific vertex branching
The application aims at HIV infection related intestinal flora, smoking related oral flora, AD related skin flora, pulmonary fibrosis related respiratory flora, BV related vaginal flora and infertility related semen flora, and further comprises the following steps:
s1: obtaining a known normal flora sample at a specific site and a flora sample to be detected possibly related to diseases, extracting DNA, amplifying and sequencing 16s rDNA, and obtaining bacterial species composition information of each flora sample by using a standard biological information analysis process based on a sequencing result, namely an operation classification unit, wherein each operation classification unit represents a species, and the sequencing content of the operation classification unit in each sample represents the species abundance of the operation classification unit in the sample;
s2: taking the composition of flora bacterial species and the abundance of each bacterial species as input;
s3: respectively constructing an interaction network of a normal flora and a flora to be detected based on the interaction relationship among bacteria;
s4: counting the number of the 15 target markers in each interaction network;
s5: calculating the numerical ratio of the residual target markers of the flora to be detected and the normal flora, and recording the numerical ratio as RDHT;
s6: and (4) judging the abnormal conditions of the flora to be detected by taking RDHT as a screening standard, and outputting a judgment result as a device.
2. The use of a panel of bacteria interaction network markers according to claim 1 for the preparation of a device for screening for disease-associated bacteria, wherein: the application implements its algorithms and functions in any form of software and hardware.
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| CN109785898B (en) * | 2019-01-14 | 2021-03-16 | 清华大学 | Method for evaluating environmental pollution risk based on microbial network |
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| CN112151117B (en) * | 2020-08-11 | 2023-02-03 | 康美华大基因技术有限公司 | Dynamic observation device based on time series metagenome data and detection method thereof |
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